Janek Groß
Materials Chemistry · 2020
A researcher in the Science corpus: 1 paper; trusts 29 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Materials Chemistry — 18 citations into it; #1,137 of 11,513 by trust here, #5,977 by citations
- Computational Chemistry & Drug Discovery — 15 citations into it; #69 of 425 by trust here, #190 by citations
- Model Architectures — 15 citations into it
- Computational Theory and Mathematics — 14 citations into it; #1,964 of 12,182 by trust here, #1,667 by citations
- Graph Learning — 14 citations into it; #186 of 693 by trust here, #277 by citations
- Artificial Intelligence — 13 citations into it
- Computer Vision and Pattern Recognition — 11 citations into it
- AI for Physical Sciences — 8 citations into it
Whom Janek Groß trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Taco Cohen
- Risi Kondor
- A. P. Bart'ok
- Kaiming He
- Max Welling
- Geoffrey E. Hinton
- J. Behler
- Alex Krizhevsky
- Stefan Chmiela
- Diederik P. Kingma
Who trusts Janek Groß overall
- Johannes Klicpera
- Christopher Morris
- Tess E. Smidt
- Mario Geiger
- Oliver T. Unke
- Yi Liao
- Chi Chen
- Kristof T. Schütt
- Simon L. Batzner
- Albert Musaelian
Papers from Janek Groß's perspective
Ordered by how strongly the researchers Janek Groß trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Directional Message Passing for Molecular Graphs (2020) — 1,129 citations